1. Field of the Invention
The present invention relates generally to physical barriers placed along a perimeter of a security area for the purpose of thwarting or at least delaying unwanted intrusions. The barriers may be combined with sensors to enable electronic security systems and methods to automatically and reliably monitor the perimeter for intruders or terrorist threats.
2. Description of the Related Art
Security zones for protecting groups of people and/or facilities be they private, public, diplomatic, military, industrial, or other zones, can be dangerous environments for people and property if threatened by intruders. The prior art in security systems and armored protection provide some solutions but fall far short of being synergistically integrated and are often are too costly and require intense human oversight. Solutions that include the use of sensors have been limited by lower than desirable probability of detection of intrusion attempts, by higher than desirable nuisance alarm rates (NAR), and by higher than desirable false alarm rates (FAR).
In the prior art, automated monitoring and control systems sense disturbances to an ambient condition and cause alarms to be activated, but these systems fall short of being able to adequately identify many relevant cause(s) of a disturbance, and they are not usually applied to detecting attempts at physical intrusion through a physical barrier. U.S. Patent Application Publication No. 2006/0031934 by Kevin Kriegel titled “Monitoring System”, incorporated herein by reference in its entirety, discloses a system that monitors and controls devices that may sense and report a location's physical characteristics through a distributed network. Based on sensed characteristics, the system may determine and/or change a security level at a location. The system may include a sensor, an access device, and a data center. The sensor detects or measures a condition at a location. The access device communicates with the sensor and the data center. The data center communicates with devices in the system, manages data received from the access device, and may transmit data to the access device. However this discloses nothing to provide a physical barrier against intruders accessing the devices that are to be monitored.
Rows of concrete barrier blocks that can slide across the ground can stop and destroy terrorist vehicles that collide with them, and can protect against blast waves and blast debris, but they offer no earlier warning signals of threats. U.S. Pat. No. 7,144,186 to Roger Allen Nolte titled “Massive Security Barrier”, U.S. Pat. No. 7,144,187 to Roger Allen Nolte and Barclay J. Tullis titled “Cabled Massive Security Barrier”, U.S. Pat. No. 7,654,768 to Barclay J. Tullis, Roger Allen Nolte, and Charles Merrill titled “Massive Security Barriers Having Tie-Bars in Tunnels”, and U.S. Pat. No. 8,061,930 to Barclay J. Tullis, Roger Allen Nolte, and Charles Merrill titled “Method of Protection with Massive Security Barriers Having Tie-Bars in Tunnels” all incorporated herein by reference in their entireties, disclose barrier blocks or modules, and barriers constructed of barrier modules. U.S. Pat. No. 7,144,186 discloses barrier modules, each with at least one rectangular tie-bar of steel cast permanently within concrete (or other solid material) and extending longitudinally between opposite sides of the barrier module, wherein adjacent barrier modules are coupled side-against-side by means of strong coupling devices between adjacent tie-bars, and wherein no ground penetrating anchoring means is involved. But since the tie-bars are cast within the barrier modules, they cannot be changed out or upgraded without removing and replacing the solid material as well. However, U.S. Pat. No. 7,144,187 discloses barrier modules of solid material with tunnels extending between opposite sides, wherein adjacent barrier modules are coupled side-against-side with cables passing through the tunnels and anchored to sides of at least some of the barrier modules by anchoring devices. And U.S. Pat. No. 7,654,768 discloses barrier modules that have tie-bars in tunnels that extend longitudinally between opposite sides of a barrier module. U.S. Pat. No. 8,061,930 discloses methods for providing protection from a terrorist threat by using the above barrier modules that have tie-bars in tunnels. Whereas barriers of concrete blocks provide impressive protection against breeches by vehicles and explosives, they provide alone little to prevent humans from climbing over them.
U.S. Pat. No. 8,210,767 to David J. Swahlan and Jason Wilke titled, “Vehicle Barrier with Access Delay” discloses an access delay vehicle barrier for stopping unauthorized entry into secure areas by a vehicle ramming attack. The barrier disclosed includes access delay features for preventing and/or delaying an adversary from defeating or compromising the barrier. A horizontally deployed barrier member can include an exterior steel casing, an interior steel reinforcing member and access delay members disposed within the casing and between the casing and the interior reinforcing member. Access delay members can include wooden structural lumber, concrete and/or polymeric members that in combination with the exterior casing and interior reinforcing member act cooperatively to impair an adversarial attach by thermal, mechanical and/or explosive tools. However, this solution alone does little to prevent humans from easily climbing over or under its structure.
In a paper titled, “A low cost fence impact classification system with neural networks” by J. de Vries in the 7th AFRICON Conference in Africa, 17 Sep. 2004, Vol. 1, pp. 131-136, a system is proposed for securing property to prevent livestock theft and farm intrusions. The paper reports a system that analyzes vibrations sensed by a point sensor to detect intrusions past a game farm or security fence, and since the point sensor can detect vibrations generated at a distance from the sensor, owners of protected property can receive early warnings. Different types of intrusions can be distinguished if they generate different vibrations. But use is made of only one type of sensor, a point vibration sensor on each horizontal wire of a wire fence. Avoiding challenges of dealing with signals varying in amplitude and duration caused by variation in distances of fence disturbances from a sensor, the author chose to use cross-correlations to detect events on wires and then input those events as ones into a feature set defined by wire number and time slots.
In the 2004 Proceedings of the 37th Hawaii International Conference on System Sciences, a paper titled, “Intrusion Sensor Data Fusion in an Intelligent Intrusion Detection System Architecture”, by Ambareen Siraj, Rayford B. Vaughn, and Susan M. Bridges, the authors state, “most modern intrusion detection systems employ multiple intrusion sensors to maximize their trustworthiness.” They also say, “The overall security view of the multisensory intrusion detection system can serve as an aid to appraise the trustworthiness in the system.” Their paper presents their research effort in that direction by describing a Decision Engine for an Intelligent Intrusion Detection System (IIDS) that fuses information from different intrusion detection sensors using an artificial intelligence technique. The Decision Engine uses Fuzzy Cognitive Maps (FCMs) and fuzzy rule-bases for causal knowledge acquisition and to support the causal knowledge reasoning process. However, their paper deals only with detecting intrusions into electronic communication traffic and does not anticipate utilizing interactions of sensors with elements of a physical barrier structure, and it does not disclose use of sensors that corroborate one another in a complementary way by virtue of being physically connected to a common structure experiencing a disturbance.
U.S. Pat. No. 5,091,780 by Pomerleau titled, “A trainable security system and method for the same”, discloses a security system comprising a processing device for monitoring an area under surveillance by processes images of the area to determine whether the area is in a desired state or an undesired state. The processing device is said to be trainable to learn the difference between the desired state and the undesired state. The processing device includes a computer simulating a neural network. However, it is well known that image sensors use limited fields of view, and that neural nets operating on imaging data can be fooled by camouflaged intruders, very rapid changes, and a wide diversity of weather.
U.S. Pat. No. 5,517,429 by Harrison titled, “Intelligent area monitoring system”, discloses an intelligent area monitoring system having a plurality of sensors, a neural network computer, a data communications network, and multiple graphic display stations. The neural network computer accepts the input signals from each sensor. It is asserted that any changes that occur within a monitored area are communicated to system users as symbols which appear in context of a graphic rendering of the monitored area to represent the identity and location of targets in the monitored area. The disclosed system attempts to identify objects by sensed attributes their locations, but is insufficient to detect or identify intrusive actions. Furthermore, “any changes” may include those scene changes responsible for what would desirably be categorized as nuisance alarms or even false alarms, and no such classification and identification is disclosed. The disclosed system doesn't comprise a physical security barrier nor is it combined with one, nor does it therefore exploit in any way the manner of mounting sensors to a common structure.
U.S. Pat. No. 8,253,563 by Burnard, et al. titled, “System and method for intrusion detection”, discloses an invention that may be employed in intruder and vehicle alarm systems. The disclosure states, “Present day intrusion detection systems frequently cause false alarms by mistaking occupants as intruders, and it is desirable to reduce such false alarms.” Their invention uses a processor that receives sensor signals over temporal periods and employs various software algorithms to statistically discern various activities, thereby attempting to reduce false alarms and detection failures. They state that the typical nature of activities is such that noise occurs frequently, normal activities occur less frequently, and abnormal activities occur least frequently. The algorithms apply logic statements to infer that information with a high probability of occurrence may be noise, information with a lower probability of occurrence may be normal activity, and information with the least probability of occurrence may be abnormal activity. Furthermore their system adjusts thresholds to obtain a predetermined false alarm rate. Something better is needed for a security barrier to reduce to a minimum both false alarm rates and nuisance alarm rates.
U.S. Pat. No. 8,077,036 by Berger et al. titled, “Systems and methods for security breach detection”, discloses a system for detecting and classifying a security breach, one that may include at least one sensor configured to detect seismic vibration from a source, and to generate an output signal that represents the detected seismic vibration. The system may further include a controller that is configured to extract a feature vector from the output signal of the sensor and to measure one or more likelihoods of the extracted feature vector relative to set of breach classes. The controller may be further configured to classify the detected seismic vibration as a security breach belonging to one of the breach classes by choosing a breach class within the set that has a maximum likelihood. But not all breeches of a fence or other physical barrier can be detected by sensing only seismic vibrations.
U.S. Pat. No. 7,961,094 by Breed titled, “Perimeter monitoring techniques”, discloses a method for monitoring borders or peripheries of installations and includes arranging sensors periodically along the border at least partially in the ground, the sensors being sensitive to vibrations, infrared radiation, sound or other disturbances, programming the sensors to wake-up upon detection of a predetermined condition and receive a signal, analyzing the signal and transmitting a signal indicative of the analysis with an identification or location of the sensors. The sensors may include a processor embodying a pattern recognition system trained to recognize characteristic signals indicating the passing of a person or vehicle. Whereas it is disclosed to apply pattern recognition techniques to each sensor individually, what is needed are more powerful techniques that apply pattern recognition techniques to a set of sensors as a whole, and in particular to a group of sensors of different types.
In a paper titled, “Machine Learning that Matters”, by Kiri L. Wagstaff, published in the Proceedings of the Twenty-Ninth International Conference on Machine Learning (ICML), June 2012, it is stated that much of current machine learning (ML) research has lost its connection to problems of import to the larger world of science and society. What are needed are more applications of machine learning techniques to real-world applications such as improving the probabilities of detection of intruder or terrorist activities while minimizing false alarms rates and nuisance alarm rates.